Ridesharing has become popular over the last decade, bringing significant changes to residents’ travel patterns and city traffic. Relevant to the growth and impact of ridesharing activities in populous urban areas are the side effects, such as air pollution and traffic congestion. Despite a number of studies on these side effects, few have focused on their underlying causes and explained the behavioral incentive. Drawing on the theory of averting behavior, this paper investigates the ridesharing choices of individuals who face but would prefer to avoid ambient air pollution. Such air pollution–averting behavior tends to increase the demand for ridesharing services, which further results in more motorized traffic and pollutant emissions. Utilizing trip records from DiDi company and data on air pollutants in Haikou, China, we find that an additional 10 μg/m³ of PM_2.5 leads to an increase of 2.6% in the count of DiDi trip orders. Results also show that traffic congestion increases due to elevated DiDi usage and the effect varies with time. This could further worsen the air quality and bring other potential side effects. These empirical results offer unique insights into the impact of air pollution on ridesharing and traffic from the perspective of individual behavioral motivation.